The Canonical Regression Quantile method predicts CEO compensation and future performance.
problem Determining fair CEO compensation and its impact on company performance.
method Canonical Regression Quantile method to assess CEO pay and performance.
result The method can predict future CEO performance and distinguish over/underpaid CEOs.
Study proposes new framework for Board-CEO relationship.
problem Recent corporate failures highlight Board's role in governance effectiveness.
method Analyzes Board-CEO relationship and role reversal in Principal-Agent Theory.
result Role reversal in governance may lead to organizational collapse.
Higher CEO career breadth correlates with better firm performance.
problem Limited adaptability in complex environments due to specialization.
method Constructed a Breadth Index from 650 CEOs' cross-domain experience, analyzed using regression.
result Higher Breadth Index CEOs outperform industry peers by 9.8 percentage points.
Optimizes causal effects on unknown graphs using Causal Entropy Optimization.
problem Optimizing causal effects in unknown causal graphs.
method Causal Entropy Optimization (CEO) framework that generalizes Causal Bayesian Optimization (CBO). Incorporates causal structure uncertainty in surrogate models and intervention selection.
result CEO achieves faster convergence to global optimum compared to CBO and improves upon sequential structure learning.
The high pay packages of U.S. CEOs have raised serious concerns about what would constitute a fair pay.
Study examines how social media sentiment impacts biotech stocks.
problem Understanding the impact of social media on biotech stock prices.
method VADER sentiment analysis, ARIMA, and VAR models were used to forecast stock market performance.
result Complex interplay between tweet sentiment and stock market performance was identified.
Based on 46 in-depth interviews with scientists, engineers, and CEOs, this document presents a list of concrete machine research problems, progress on which would directly benefit tech ventures in East Africa.
This paper studies communication efficiency in federated learning by optimizing the sum-rate-distortion function for indirect multiterminal source coding.
problem Indirect multiterminal source coding in federated learning where edge devices send noisy gradients to the server.
method Analyzes the rate region for the quadratic vector Gaussian CEO problem under unbiased estimator and derives an explicit formula for the sum-rate-distortion function.
result Derives an explicit formula for the sum-rate-distortion function in the special case of identical gradients over edge devices.
LLMs help less-resourced researchers access costly data.
problem Unequal access to costly datasets limits research contributions.
method RAG framework with GPT-4o-mini for automated data collection.
result LLMs can collect CEO pay ratios and CAMs from corporate disclosures with high accuracy and low cost.
Study shows how business cycle affects dividend payout based on managerial stock incentives.
problem Impact of managerial stock incentives on dividend payout policy during business cycles.
method Using S&P 1500 companies data from 2000-2018, analyzing full sample and recession periods.
result Negative relationship between managerial stock options and dividend payouts, significant for medium-sized companies.
Out-of-control information technology (IT) projects have ended the careers of top managers, such as EADS CEO Noel Forgeard and Levi Strauss' CIO David Bergen. Moreover, IT projects have brought down whole companies, like Kmart in the US and Auto Windscreen in the UK. Software and other IT is now such an integral part o…
This paper tests LLMs in finance to assess ethical behavior.
problem Aligning AI with ethical and legal standards in finance.
method Prompted LLMs to simulate CEO behavior, analyzed with logistic regression.
result Significant heterogeneity in LLMs' unethical behavior propensity.
Studies have shown that the people depicted in image search results tend to be of majority groups with respect to socially salient attributes. This skew goes beyond that which already exists in the world - e.g., Kay et al. showed that although 28% of CEOs in US are women, only 10% of the top 100 results for CEO in Goog…
Rebellion Research's AI strategy outperformed the S&P 500 for 14 years.
problem Outperforming the S&P 500 for 14 years with AI.
method AI Global Equity strategy focused on machine learning.
result AI strategy has outperformed the S&P 500 for 14 years.
The excessive compensation packages of CEOs of U.S. corporations in recent years have brought to the foreground the issue of fairness in economics. The conventional wisdom is that the free market for labor, which determines the pay packages, cares only about efficiency and not fairness. We present an alternative theory…
Paper studies fundamental limits of communication in distributed learning.
problem Communication efficiency in model aggregation for distributed learning.
method Rate-Distortion approach to model aggregation as a vector Gaussian CEO problem.
result Derives rate region bound and sum-rate-distortion function for model aggregation.
The widening inequality in income distribution in recent years, and the associated excessive pay packages of CEOs in the U.S. and elsewhere, is of growing concern among policy makers as well as the common person. However, there seems to be no satisfactory answer, in conventional economic theories and models, to the fun…
Paper analyzes tech adoption in financial networks, finding key leadership and diffusion dynamics.
problem Understanding technology adoption and network effects in financial systems.
method Developed a spatial-network framework with a master equation and Feynman-Kac representation.
result Found strong support for two-regime adoption dynamics and significant leadership in network central banks.
Study introduces new financial ratios for better predicting company performance.
problem Lack of progress in predicting company performance and assessing financial risks.
method Developed new financial and macroeconomic ratios, supervised learning models, and Bayesian models.
result New proposed variables improve model accuracy and FNN performs best across multiple tasks.
Study earnings calls to predict stock price movements, finding them more predictive than traditional data.
problem Improving investment decisions by analyzing earnings calls for stock price predictions.
method Graph Neural Network based approach to process and analyze earnings call transcripts.
result Earnings call transcripts are more predictive of stock price movements than traditional hard data.